1. Field of the Invention
The present invention relates to information processing apparatus and method for detecting an object in image data.
2. Description of the Related Art
In recent years, various kinds of methods of detecting and identifying a specific object in image data have been proposed and put into practical use.
Among them, a method disclosed in Viola, P.; Jones, M., “Rapid object detection using a boosted cascade of simple features”, Proc. of Computer Vision and Pattern Recognition, 2001-12, IEEE Computer Society, pages 511-518, is highlighted because of its high processing speed. The method of the above thesis is a method whereby strong discriminators each constructed by a plurality of weak discriminators which are formed by a boosting learning algorithm are cascade-connected and processes are progressed while making a truncation discrimination (forced termination of processes to a detection target position) every strong discriminator. FIG. 1 is a diagram illustrating a constructional concept of the system disclosed in the above thesis. Discriminators 31 to 3m (True: There is detection target object, False: Detection target object does not exist) which are formed by learning are constructed by a plurality of simple discriminating filters of a small processing load. Each discriminating filter is called a weak discriminator because its discriminating ability is not so high. The discriminators (31 to 3m) in which results of a plurality of weak discriminators are integrated based on reliability values, which will be described hereinafter, are called strong discriminators. For simplicity of description, the strong discriminator constructed by a plurality of weak discriminators formed by the boosting learning algorithm is called a boosting discriminator. FIG. 2 is a diagram illustrating an internal construction of each of the boosting discriminators 31 to 3m. Weak discriminators 411 to 41n and a threshold value processor 420 for integrating discrimination results of the weak discriminators and discriminating are illustrated.
In the above thesis, each of the weak discriminators 411 to 41n is constructed by an extremely simple discriminating filter called a rectangle filter. FIG. 3 is a diagram illustrating an example of the rectangle filter. Image blocks 51a to 51c serving as detection targets are partial area images of a predetermined size (size including an object as a detection target) cut out of the whole image. In FIG. 4, a whole image data 61 as a detection target and a partial area image 62 corresponding to 51a to 51c are illustrated. The partial area image is referred to as a detection window hereinbelow. The weak discriminator scans the detection window by a predetermined step size and identifies on a detection window unit basis, thereby deciding the presence or absence of the specific object in the image and its position. Examples of rectangle filters 52a to 52c are illustrated. The weak discriminator discriminates the existence of the object by setting a difference between pixel value sums of the areas shown by a white area and a black area to a feature of a local area in the detection window.
As illustrated in FIGS. 2 and 3, the boosting discriminator uses a number of weak discriminators having different characteristics, thereby finally realizing an identifying function (strong discrimination) of high discriminating performance.
A method of efficiently learning such a boosting discriminator has been proposed in Japanese Patent Application Laid-Open No. 2005-284348.